A robust inverse regression estimator

    Authors

    L. Q. Ni;R. D. Cook

    Comments

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    Abbreviated Journal Title

    Stat. Probab. Lett.

    Keywords

    central subspace; inverse regression estimator; sufficient dimension; reduction; SUFFICIENT DIMENSION REDUCTION; Statistics & Probability

    Abstract

    A family of dimension reduction methods was developed by Cook and Ni [Sufficient dimension reduction via inverse regression: a minimum discrepancy approach. J. Amer. Statist. Assoc. 100, 410-428.] via minimizing a quadratic objective function. Its optimal member called the inverse regression estimator (IRE) was proposed. However, its calculation involves higher order moments of the predictors. In this article, we propose a robust version of the IRE that only uses second moments of the predictor for estimation and inference, leading to better small sample results. (c) 2006 Elsevier B.V. All rights reserved.

    Journal Title

    Statistics & Probability Letters

    Volume

    77

    Issue/Number

    3

    Publication Date

    1-1-2007

    Document Type

    Article

    Language

    English

    First Page

    343

    Last Page

    349

    WOS Identifier

    WOS:000243660000015

    ISSN

    0167-7152

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